Distributed Filter Using ADMM for Optimal Estimation Over Wireless Sensor Network

Abstract

This paper addresses a distributed filter over wireless sensor networks for optimal estimation. A distributed filter over the networks allows all local estimators to calculate optimal estimates with a scalable communication cost. Outputs of the distributed filter for the optimal estimation can be denoted as a solution of a consensus optimization problem. Thus, the distributed filter is designed based on distributed alternating direction method of multipliers (ADMM). The remarkable points of the distributed filter based on the ADMM are that: the distributed filter has a faster convergence rate than distributed subgradient projection algorithm; the weight, which is optimized by a semidefinite programming problem, accelerates the convergence rate of the proposed method.